Sunday, June 7, 2026

AI Infra Financing Gets Creative

Financing of AI infrastructure has evolved into a complex, multi-layered financial architecture that extends well beyond traditional corporate balance sheets. 


External financing structures include:

  • Strategic partnerships: Frontier model labs and hyperscalers are forming partnerships for regional development, power infrastructure, and equity contributions

  • Public sector and sovereign support

  • Captive markets: In some instances, state-owned enterprises or governments direct domestic demand toward local chip manufacturers.


Financing Model

Description

Example / Context

Source

Structured/Off-Balance Sheet

Using infrastructure funds and private credit to distribute risk across a layered set of claims.

General industry shift toward using private credit and structured vehicles to fund data center buildouts.

BIS

Community-First Partnerships

Joint commitments between developers and providers to share infrastructure costs and regional responsibilities.

Microsoft's "Community-First AI Infrastructure" plan and OpenAI's "Stargate Community" initiative.

HKS

National Sovereign Investment

Coordinating investments in data, compute, and algorithms through sovereign-backed frameworks.

Frameworks for "AI Triads" in low-to-middle-income countries using structured funding tranches.

Oxford

Captive Market Funding

Generating revenues through domestic mandated demand to fund internal R&D cycles.

Huawei’s AI chip revenue generation within the Chinese domestic ecosystem.

Bruegel


In many instances, the intent is to reduce capital investment requirements by moving to off balance sheet vehicles or “compute as payment” arrangements.


Hyperscaler

Model Supplier

Deal Type / Structure

Estimated Value / Capacity

Source

Google

Anthropic

Multi-year compute commitment + Equity investment

Up to $40B investment; 3.5GW TPU capacity (via Broadcom)

Silicon Republic

Amazon (AWS)

Anthropic

Compute credit + Equity investment

Up to $25B total commitment; multi-year cloud compute

Silicon Republic

Microsoft

OpenAI

Exclusive cloud provider + Multi-stage capital injection

~$10B+ in multi-year funding; 49% profit stake

Aranca

Meta

N/A (Self-build)

Structured finance (SPV) for data center buildout

~$30B "Hyperion" SPV (Blue Owl Capital led)

SoftwareSeni

Google/Anthropic

SpaceX

Compute infrastructure delivery contracts

Potentially >$70B over multi-year term

AA


As seen with Meta’s "Hyperion" transaction, hyperscalers are increasingly utilizing Special Purpose Vehicles (SPVs) and partnerships with private credit firms (e.g., Blue Owl Capital) to fund massive data center buildouts. This allows the companies to offload the capital intensity of the physical build while retaining operational control and capacity priority.


In many of these deals, "compute" has become a literal form of payment. The Google-Anthropic and Amazon-Anthropic deals are not merely cash-for-equity; they are deeply intertwined with multi-gigawatt (GW) capacity commitments and customized hardware access (such as Google’s TPUs).


Financing is no longer focused just on chips. The capital is increasingly directed toward the "AI Triad"—the integration of compute, dedicated energy infrastructure, and data center physical shells. This is evidenced by the trend of co-locating data centers with renewable energy sources and the invocation of national defense acts (as seen in the U.S. in early 2026) to prioritize grid expansion for AI.


SpaceX GPU Deal with Google Cloud is About Enterprise Nvidia Compute Demand

Google has signed a deal deal with SpaceX for access to graphics processing unit compute amounting to about $31.5 billion for three years, suggesting just how massive Google believes its own artificial intelligence computing needs may be, especially to support enterprise customers. 


The deal gives Google access to some 110,000 Nvidia GPUs, beginning in October 2026. 


Some will see this as a bridge to the time when Google can bring its owned compute facilities online at the scale Google believes is necessary. 


Gigawatt-scale data centers take years to build, so the deal is a hedge that allows Google to supply 

capacity other “compute as a service” suppliers might otherwise take. 


Enterprise customers prefer NVIDIA’s CUDA ecosystem. So leasing Nvidia capacity from SpaceX allows Google Cloud to sell what enterprise clients want without diverting its custom accelerator (tensor processing units) supply away from its own model development and inferencing.


By some estimates, Nvidia GPUs, such as the H100 SXM5 nodes, will remain in short supply until 2027.  


GPU

Typical Lead Time (Direct Purchase)

Cloud Availability

H100 SXM5

36-52 weeks

Limited on hyperscalers; neo-clouds

H200 SXM5

40+ weeks

Reserved pools mostly sold out

B200

Allocated through H2 2027

Limited to select providers

A100 80GB

8-16 weeks

More available; watch for constrained VRAM 

L40S

4-8 weeks

Good availability; strong for inference


Google is paying about $8,363 per GPU per month, or roughly $11.45 per hour assuming the machines run around the clock. 


Top-tier NVIDIA chips on major cloud platforms currently go for perhaps $5 to $15 per hour, depending upon instance types, with newer architectures like the Blackwell or Reubin LPX for Agentic AI applications perhaps costing from $15 to $50 per hour. 


By some estimates estimates, Google will layer its own software, support, and service guarantees on top of the SpaceX infrastructure, then charge enterprise customers somewhere between $18 and $70 per GPU per hour. 


For SpaceX, set to go public on June 12, 2026, the additional revenue will undoubtedly provide an underpinning for the new public firm’s valuation. The deal adds about $950 million per month of committed revenue. 


Many expect the SpaceX IPO will be the largest in history.


Friday, June 5, 2026

Butterfly Effect: 100% Deterministic and Yet 0% Predictable

Maybe you have been puzzled by the butterfly effect, the idea that a tiny flap of a butterfly's wings in one part of the world can fundamentally alter weather patterns weeks later. 


The core concept really is not the flap of a butterfly’s wings, but the idea that highly-chaotic systems are highly sensitive on initial conditions. 


In stable (linear) systems, small errors in measurement result in proportionally small errors in forecasting. 


In chaotic systems, however, uncertainty grows exponentially over time. As mathematician Edward Lorenz observed, doubling your observation accuracy only pushes your reliable prediction window forward by a tiny, fixed interval rather than doubling it.


In highly-chaotic systems, sub-microscopic variables such as a fraction of a degree in temperature or a microscopic shift in air pressure can quickly snowball into macroscopic outcomes, rendering long-term forecasting impossible.


Every chaotic system has a distinct timeframe known as the Lyapunov time—the duration over which a system remains predictable before the errors outpace the meaningful data. 


This time scale varies drastically depending on the system:

  • Electrical circuits: ~1 millisecond

  • Global weather patterns: ~1 to 2 weeks

  • The inner solar system: ~4 to 5 million years.

Beyond a system's specific Lyapunov time, forecasts essentially degrade into educated guesses and the system appears entirely random.


The butterfly effect severely complicates prediction in a wide variety of highly dynamic, non-linear complex systems:

  • Economics & Finance: Seemingly minor shifts in supply, policy, or public sentiment can trigger cascading market reactions or crashes

  • Epidemiology: The initial outbreak location and minor mutations of a virus can drastically alter the spread and severity of global pandemics.

  • Ecology: Removing or introducing a single predatory species can cause unforeseen, system-wide environmental collapses.


Oddly enough, I find, chaos theory is strictly deterministic, yet unpredictable.  


The future is completely determined by the past, with zero randomness involved: 

  • No Randomness: If you could input the exact same initial conditions twice, a chaotic system would yield the exact same output every time.

  • The Catch: You can never measure initial conditions perfectly.

  • The Result: Even a microscopic difference in your starting data alters the final result entirely.


Chaos theory means a system can be 100-percent deterministic while remaining zero percent predictable in the long term.


Crazy!


"Magnifica Humanitas" is No "Rerum Novarum"

At the risk of seemingly disagreeing with "Magnfica Humanitas," it is still possible to compare that document with Rerum Novarum, upon which the new encyclical is based, and see clear differences, beyond the specific problems tackled by each document.


At the risk of downplaying artificial intelligence impact, which many could characterize as a general-purpose technology that will transform nearly every industry, the encyclical Rerum Novarum ("Of New Things"), issued by Pope Leo XIII on May 15, 1891, was not addressed “merely” to the impact of the industrial revolution on workers.


Laissez-faire economics; private property; socialist and Marxist ideas were paramount issues also tackled by Rerum Novarum. 


To the extent that Pope Leo XIV’s encyclical Magnifica Humanitas is modeled purposefully on Rerum Novarum, we can compare the two documents.


To be sure, Rerum Novarum focused on:

  • potential exploitation of the working class

  • Protecting workers

  • Materialism, moral and spiritual issues

  • ideological extremes (unregulated capitalism and socialism).


But Rerum Novarum also clearly established some clear and practical guidance for Catholic social teaching that are unmatched by any other religion or spiritual belief. 


Catholic social teaching means the Catholic church is clearly and officially:

  • Opposed to socialism and collectivist economics

  • A supporter of the fundamental right of private property

  • A supporter of the right to form trade unions and other intermediate social institutions

  • A supporter of market-based economies. 


Magnifica Humanitas is focused on AI’s impact on human dignity. And, to be sure, it warns of the dangers of concentrated “technocratic” power. 


But calls for ethical governance, transparency, accountability, subsidiarity (participation by communities), solidarity and orienting technology toward the common good and human flourishing are not in the same league as opposing both unrestrained capitalism and socialism (communism). 


Rerum Novarum defended the right of private property, for example. So Magnifica Humanitas might criticize unethical behavior, but it does not call for expropriation.


Magnifica Humanitas argues for an AI that serves humanity, not dominates it. We might see that as in line with the argument of Rerum Novarum. Some possible differences are that Rerum Novarum had more direct and practical implications. 


Rerum Novarum:

  • Made opposition to socialism foundational for Catholic social teaching

  • Specifically supported the role of labor unions and other social groupings

  • Supports private property rights as essential for human freedom and creativity

  • Supports market-based economics. 


Magnifica Humanitas, in my reading, includes nothing similar. 


Socialists and other leftists might argue Magnifica Humanitas supports expropriation of an AI firm’s  property. Since Rerum Novarum, that is in conflict with Catholic social teaching. 


Magnifica Humanitas contains no similar institutional practices (supporting labor unions as a counterweight and many types of intermediate institutions (family, guilds, social organizations) as a way of restraining the exercise of all social power by the state. 


Magnifica Humanitas contains no new proposals for restricting market economies or embracing socialism or expropriation. 


Instead, it is a moral exhortation; a statement of principles; a broad action to exercise prudence.  


As Rerum Novarum arguably shaped moral discourse, legitimized reforms, and encouraged balanced responses over revolution, so Magnifica Humanitas attempts the same. 


Still, one might read the new document as offering few practical pillars, compared to Rerum Novarum.


Wednesday, June 3, 2026

Equity Valuations are High, But are They "Too High?"

As common as it is to compare today’s artificial intelligence equity valuations to dot-com bubble levels, there is a reasonable argument to be made that the two periods are not similar, even if market leadership spikes resemble past booms. 

source: Bank of America Global Research, Leo Nelissen 


Of course, we might also note that each valuation boom “ended” at some point, with valuations normalizing. 


So it is rational to expect a repeat. 


On the other hand, the comparisons might be wrong. 


Forward price-earnings ratios for market leaders are nowhere near internet bubble levels, though that might not be the case for smaller growth names. 

source: Ritholz Wealth Management, Leo Nelissen 


The “complication” is earnings growth. So long as that continues, so does the support for valuation.


Tuesday, June 2, 2026

Magnifica Humanitas is not a Comprehensive Statement about Total AI Impact

Rerum Novarum (Latin: “Of New Things”) is an encyclical issued by Pope Leo XIII in 1891 that addressed the social and economic problems caused by the Industrial Revolution for workers and laid the foundation for modern Catholic social teaching.


To the extent that Pope Leo XIV’s encyclical Magnifica Humanitas is modeled on Rerum Novarum, we might also apply some of the same observations which can be made about the earlier document. 


An argument can be made that the encyclical focused on people in their role as workers, not in their role as consumers, arguably an issue that remains relevant today whenever policymakers talk about what is “good for working people.”  


People occupy multiple economic roles simultaneously:

  • Workers (selling labor)

  • Consumers (buying goods and services)

  • Producers/owners (running businesses or owning capital)

  • Citizens/community members (experiencing social and environmental effects). 


The Industrial Revolution affected each role differently, often creating losses in one dimension while generating gains in another. By extension, the same will probably be true for the concerns of Magnifica Humanitas. 


The Industrial Revolution created enormous increases in productivity, which led to:

  • Lower prices for goods

  • Greater product availability

  • Rising real incomes over time

  • Improved transportation and communication

  • Longer life expectancy (eventually)

  • Better housing, nutrition, and health outcomes (over the long run).


But as often happens, losses are concentrated and obvious while benefits are diffuse and hard to measure. 


Losses were concentrated among some worker segments, such as weavers, blacksmiths, coach drivers or other craftsmen whose jobs were automated.


But the same people often benefited as well. 


Consumer Benefit

Examples

Lower prices

Clothing, household goods, food transport

Greater variety

Previously unavailable products

Better quality

Standardized manufactured goods

Improved access

Railroads connected markets

Rising purchasing power

Real wages eventually increased


What was bad for some textile workers as producers often became beneficial for nearly everyone as consumers, a recurring pattern in economic change: losses are concentrated; gains are widespread and diffuse. 


The losers know exactly who they are. The beneficiaries are nearly everyone.


Even if long-run outcomes were positive, some occupations and generations bore significant transitional costs.


Industrial regions often prospered while other regions declined, with the negative effects you would expect on particular communities.


Consider the Industrial Revolution as creating two simultaneous realities:

Role

Typical Effect

Worker in disrupted industry

Often negative

Consumer

Usually positive

Entrepreneur

Often strongly positive

Society as a whole

Strongly positive over the long run


A handloom weaver in 1810 might be worse off as a producer but better off as a consumer. His or her children or grandchildren might eventually be substantially better off in both roles.


This framework will almost certainly be true of AI as well. 


The question is not simply whether AI helps or hurts "workers." People are simultaneously:

  • Workers whose tasks may be automated or augmented

  • Consumers who may receive cheaper and better services

  • Investors whose retirement savings may benefit from productivity growth

  • Citizens affected by broader economic changes.


The historical evidence supports the proposition that Industrial Revolution benefits ultimately became widespread and enormous, while many of the costs were concentrated among particular occupations, regions, and generations. The caveat is that for those who bore the costs, the losses were often severe, immediate, and personally significant even when society as a whole became much richer.


Magnifica Humanitas follows a similar pattern to Rerum Novarum. The warnings are stark; there is danger of dehumanization. The document does not address the almost-certain advantages and upside, anymore than did Rerum Novarum. 


As a moral argument about preserving human dignity and values, Rerum Novarum “succeeded.” But people are workers and consumers; creators or products as well as those who use them. 


The document did not address those aspects, being concerned solely with the impact of industrial production on people as workers. 


Magnifica Humanitas follows the same pattern, warning about dehumanizing AI impacts. The document does not attempt to assess the broad AI impact that might also be socially quite positive. 


The use of the term “tower of Babel” (Genesis 11:1–9) provides some insight to the framing. In Catholic theology, the passage is a warning about human pride and disregard for human dependence on God. 


So as with Rerum Novarum, Magnifica Humanitas seeks to guide action in terms of stewardship of technology to serve human ends and dignity. 


It is not a comprehensive statement about AI’s overall impact.


AI Infra Financing Gets Creative

Financing of AI infrastructure has evolved into a complex, multi-layered financial architecture that extends well beyond traditional corpora...